Plant Disease Detection System using Bag of Visual Words

Full Text (PDF, 397KB), PP.57-63

Views: 0 Downloads: 0

Author(s)

D. Asir Antony Gnana Singh 1,* E. Jebamalar Leavline 1 A. K. Abirami 1 M. Dhivya 1

1. Anna University, BIT Campus, Tiruchirappalli, 620 024, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2018.09.07

Received: 14 Jun. 2018 / Revised: 3 Jul. 2018 / Accepted: 14 Jul. 2018 / Published: 8 Sep. 2018

Index Terms

Bag of Visual Words, Plant disease detection, Speeded up robust features (SURF), Support vector machine (SVM)

Abstract

Plants are important to human life since plants provide the food, shelter, rain, building material, medicine, fuel such as coal, wood, etc. Therefore, planting, growing, and protecting the plants is essential for sustainable development of any nation. The plant disease can affect the growth of the plats that is caused by pathogens, living microorganisms, bacteria, fungi, nematodes, viruses, and living agents. Hence, identifying the plant disease is very essential to protect the plants in the early stage. Moreover, the plant diseases are identified from the symptoms that appear in stem, fruit, leaf, flower, root, etc. The common symptom of the plant disease can be predicted from the appearance of leaf since the appearance of leaves highly depends on the healthiness of the plant. Therefore, this paper presents a system to identify the lesion leaf from the plants in order to detect the disease occurred in the plant. This system is developed using the bag of visual words model. Moreover, the real time images are collected for various plants and tested with this system and the system produces better results for the given set of images.

Cite This Paper

D. Asir Antony Gnana Singh, E. Jebamalar Leavline, A. K. Abirami, M. Dhivya, "Plant Disease Detection System using Bag of Visual Words", International Journal of Information Technology and Computer Science(IJITCS), Vol.10, No.9, pp.57-63, 2018. DOI:10.5815/ijitcs.2018.09.07

Reference

[1]Singh, D.A.A.G., Leavline, E.J., Priyanka, V. and Swathi, V., 2016. Agriculture classification system using differential evolution algorithm. International Advanced Research Journal in Science, Engineering and Technology, 3, pp.24-28.

[2]Singh, D.A.A.G. and Leavline, E.J., 2014. A Pragmatic Approach on Knowledge Discovery in Databases with WEKA. International Journal of Engineering Technology and Computer Research (IJETCR), 2(7), pp.81-87.

[3]Ganesan, P., Sajiv, G. and Leo, L.M., 2017, March. CIELuv color space for identification and segmentation of disease affected plant leaves using fuzzy based approach. In Science Technology Engineering & Management (ICONSTEM), 2017 Third International Conference on (pp. 889-894). IEEE.

[4]Rastogi, A., Arora, R. and Sharma, S., 2015, February. Leaf disease detection and grading using computer vision technology & fuzzy logic. In Signal Processing and Integrated Networks (SPIN), 2015 2nd International Conference on (pp. 500-505). IEEE.

[5]de Luna, R.G., Baldovino, R.G., Cotoco, E.A., de Ocampo, A.L.P., Valenzuela, I.C., Culaba, A.B. and Gokongwei, E.P.D., 2017, December. Identification of philippine herbal medicine plant leaf using artificial neural network. In Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2017 IEEE 9th International Conference on (pp. 1-8). IEEE.

[6]Khirade, S.D. and Patil, A.B., 2015, February. Plant disease detection using image processing. In Computing Communication Control and Automation (ICCUBEA), 2015 International Conference on (pp. 768-771). IEEE. 

[7]Sengar, N., Srivastava, A. and Dutta, M.K., 2017, November. Machine vision based detection of ageratum enation virus infection using light microscopic images of poppy plants cells. In Emerging Trends in Computing Communication Technologies (ICETCCT), International Conference on (pp. 1-4). IEEE.

[8]Mai, X. and Meng, M.Q.H., 2016, June. Automatic lesion segmentation from rice leaf blast field images based on random forest. In Real-time Computing and Robotics (RCAR), IEEE International Conference on (pp. 255-259). IEEE.

[9]Asfarian, A., Herdiyeni, Y., Rauf, A. and Mutaqin, K.H., 2013, November. Paddy diseases identification with texture analysis using fractal descriptors based on fourier spectrum. In Computer, Control, Informatics and Its Applications (IC3INA), 2013 International Conference on (pp. 77-81). IEEE.

[10]Phadikar, S. and Sil, J., 2008, December. Rice disease identification using pattern recognition techniques. In Computer and Information Technology, 2008. ICCIT 2008. 11th International Conference on (pp. 420-423). IEEE.

[11]Waghmare, H., Kokare, R. and Dandawate, Y., 2016, February. Detection and classification of diseases of Grape plant using opposite colour Local Binary Pattern feature and machine learning for automated Decision Support System. In Signal Processing and Integrated Networks (SPIN), 2016 3rd International Conference on (pp. 513-518). IEEE. 

[12]Gavhale, K.R., Gawande, U. and Hajari, K.O., 2014, April. Unhealthy region of citrus leaf detection using image processing techniques. In Convergence of Technology (I2CT), 2014 International Conference for (pp. 1-6). IEEE.

[13]Ramakrishnan, M., 2015, April. Groundnut leaf disease detection and classification by using back probagation algorithm. In Communications and Signal Processing (ICCSP), 2015 International Conference on (pp. 0964-0968). IEEE.

[14]Tigistu, G. and Assabie, Y., 2015, September. Automatic identification of flower diseases using artificial neural networks. In AFRICON, 2015 (pp. 1-5). IEEE.

[15]Francis, J. and Anoop, B.K., 2016, March. Identification of leaf diseases in pepper plants using soft computing techniques. In Emerging Devices and Smart Systems (ICEDSS), Conference on (pp. 168-173). IEEE.

[16]Megha P Arakeri, Malavika Arun, Padmini R K,"Analysis of Late Blight Disease in Tomato Leaf Using Image Processing Techniques", International Journal of Engineering and Manufacturing(IJEM), Vol.5, No.4, pp.12-22, 2015.DOI: 10.5815/ijem.2015.04.02

[17]Chit Su Hlaing, Sai Maung Maung Zaw., 2017. Plant Diseases Recognition for Smart Farming Using Model-based Statistical Features. 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE 2017)

[18]Prasad, S., Peddoju, S.K. and Ghosh, D., 2014, April. Energy efficient mobile vision system for plant leaf disease identification. In Wireless Communications and Networking Conference (WCNC), 2014 IEEE (pp. 3314-3319). IEEE.

[19]Zare, M.R., Mueen, A. and Seng, W.C., 2013. Automatic classification of medical X-ray images using a bag of visual words. IET Computer Vision, 7(2), pp.105-114.